论文标题
在扭曲的空间中层次模块化动力学神经网络放松：基本模型及其特征
Hierarchically Modular Dynamical Neural Network Relaxing in a Warped Space: Basic Model and its Characteristics
论文作者
论文摘要
我们提出了一个层次模块化的动态神经网络模型，该模型将架构最小化，使设计的能量功能最小化并定义其时间特征。该模型具有一个内部和外部空间，该空间与一个分层的互联网连接，该互联网由由静态神经元组成的一对前向和后向子网组成（具有瞬时的时间表）。内部空间中具有较大时间常数的动力学神经元决定了整体时间顺序。该模型提供了一个框架，由于动态神经元和静态神经元之间的合作，网络中的状态变量在扭曲的空间中放松。我们假设该系统在学习或关联模式下运行，具体取决于反馈路径和输入端口的存在。在学习模式下，Internet工作中的突触权重通过对应于重复的神经元爆发的强输入来修改，该爆发代表神经冲动的短期平均密度或膜电位的短期平均密度，代表正弦或准螺体波。可以通过基于与Lissajous曲线相同的机制使用不同频率的信号来形成二维映射关系。在关联模式下，融合到目标点的速度随着先前训练的互联网工作的映射关系而大不相同，并且由于此属性，二维模型中的收敛轨迹与非线性映射Internet Workswork中的融合轨迹无法直接进行，而是必须弯曲。我们进一步引入了具有给定目标轨迹的约束关联模式，并阐明在内部空间中，产生了输出轨迹，该输出轨迹是根据向前子网的映射关系的倒数来从外部空间映射的。
We propose a hierarchically modular, dynamical neural network model whose architecture minimizes a specifically designed energy function and defines its temporal characteristics. The model has an internal and an external space that are connected with a layered internetwork that consists of a pair of forward and backward subnets composed of static neurons (with an instantaneous time-course). Dynamical neurons with large time constants in the internal space determine the overall time-course. The model offers a framework in which state variables in the network relax in a warped space, due to the cooperation between dynamic and static neurons. We assume that the system operates in either a learning or an association mode, depending on the presence or absence of feedback paths and input ports. In the learning mode, synaptic weights in the internetwork are modified by strong inputs corresponding to repetitive neuronal bursting, which represents sinusoidal or quasi-sinusoidal waves in the short-term average density of nerve impulses or in the membrane potential. A two-dimensional mapping relationship can be formed by employing signals with different frequencies based on the same mechanism as Lissajous curves. In the association mode, the speed of convergence to a goal point greatly varies with the mapping relationship of the previously trained internetwork, and owing to this property, the convergence trajectory in the two-dimensional model with the non-linear mapping internetwork cannot go straight but instead must curve. We further introduce a constrained association mode with a given target trajectory and elucidate that in the internal space, an output trajectory is generated, which is mapped from the external space according to the inverse of the mapping relationship of the forward subnet.
